Systems and methods for generating synthetic images of a training database

ABSTRACT

A method includes generating one or more nominal images of at least a portion of a digital twin of an environment, defining one or more anomalous characteristics of the one or more components, generating one or more anomalous images of the portion of the digital twin of the environment based on the field of view and the one or more anomalous characteristics, performing a tessellation routine and a texture mapping routine on the one or more nominal images and the one or more anomalous images to generate a plurality of synthetic images, and labeling, for each synthetic image from among the plurality of synthetic images, the synthetic image as one of an anomalous type, a nominal type, or a combination thereof.

FIELD

The present disclosure relates to systems and methods for generatingsynthetic images of a training database.

BACKGROUND

The statements in this section merely provide background informationrelated to the present disclosure and may not constitute prior art.

Artificial intelligence (AI)-based and machine learning (ML)-based imageanalysis systems may perform various supervised image-processingroutines to identify various characteristics of, for example, one ormore vehicle components. Accordingly, the image analysis systems mayiteratively receive numerous training images of the vehicle componentsto sufficiently learn how to identify the relevant vehicle componentsfrom an image and determine a characteristic of the identified vehiclecomponents (e.g., the identified vehicle component is damaged or notinstalled properly). However, current training routines utilize numerousreal images of the vehicle components and the desirable/undesirablecharacteristics, thereby requiring a substantial amount of time andeffort to sufficiently train the image analysis systems. These and otherissues associated with training image analysis systems are addressed bythe present disclosure.

SUMMARY

This section provides a general summary of the disclosure and is not acomprehensive disclosure of its full scope or all of its features.

The present disclosure provides a method for training an image analysissystem including generating one or more nominal images of at least aportion of a digital twin of an environment, where the digital twin is avirtual representation of the environment, and where the one or morenominal images are based on a field of view (FOV) of an image sensor ofthe digital twin and one or more nominal characteristics of one or morecomponents of the digital twin. The method includes defining one or moreanomalous characteristics of the one or more components, generating oneor more anomalous images of the portion of the digital twin of theenvironment based on the field of view and the one or more anomalouscharacteristics, performing a tessellation routine and a texture mappingroutine on the one or more nominal images and the one or more anomalousimages to generate a plurality of synthetic images, and labeling, foreach synthetic image from among the plurality of synthetic images, thesynthetic image as one of an anomalous type, a nominal type, or acombination thereof.

In one form, the method further includes performing a decimation routineon the one or more nominal images and the one or more anomalous imagesto generate the plurality of synthetic images. In one form, the texturemapping routine is a UV mapping routine. In one form, the field of viewis based on a nominal position coordinate of the image sensor and anominal orientation of the image sensor. In one form, the method furtherincludes performing an augmentation routine on the plurality ofsynthetic images in response to labeling the plurality of syntheticimages. In one form, the method further includes defining one or moreadjusted field of views of the image sensor, generating one or moreauxiliary nominal images of the portion of the digital twin of theenvironment based on the one or more adjusted field of views and the oneor more nominal characteristics, and generating one or more auxiliaryanomalous images of the portion of the digital twin of the environmentbased on the one or more adjusted field of views and the one or moreanomalous characteristics, where the plurality of synthetic images arefurther based on the one or more auxiliary nominal images and the one ormore auxiliary anomalous images. In one form, the one or more adjustedfield of views are based on an adjusted position coordinate of the imagesensor, an adjusted orientation of the image sensor, or a combinationthereof. In one form, the method further includes performing thetessellation routine and the texture mapping routine on the one or moreauxiliary nominal images and the one or more auxiliary anomalous imagesto generate the plurality of synthetic images. In one form, the imagesensor is disposed on one of a robot and a structure of the digitaltwin.

The present disclosure provides a system for training an image analysissystem. The system includes a processor and a nontransitorycomputer-readable medium including instructions that are executable bythe processor. The instructions include generating one or more nominalimages of at least a portion of a digital twin of the environment, wherethe digital twin is a virtual representation of the environment, andwhere the one or more nominal images are based on a field of view of animage sensor of the digital twin and one or more nominal characteristicsof one or more components of the digital twin. The instructions includedefining one or more adjusted field of views of the image sensor,generating one or more auxiliary nominal images of at least the portionof the digital twin of the environment based on the one or more adjustedfield of views and the one or more nominal characteristics, defining oneor more anomalous characteristics of the one or more components, andgenerating one or more anomalous images of the portion of the digitaltwin of the environment based on the field of view and one or moreanomalous characteristics of the one or more components. Theinstructions include generating one or more auxiliary anomalous imagesof the portion of the digital twin of the environment based on the oneor more adjusted field of views and the one or more anomalouscharacteristics, performing a tessellation routine and a texture mappingroutine on the one or more nominal images, the one or more auxiliarynominal images, the one or more anomalous images, and the one or moreauxiliary anomalous images to generate a plurality of synthetic images,and labeling, for each synthetic image from among the plurality ofsynthetic images, the synthetic image as one of an anomalous type, anominal type, or a combination thereof.

In one form, the instructions further include performing a decimationroutine on the one or more nominal images and the one or more anomalousimages to generate the plurality of synthetic images. In one form, thetexture mapping routine is a UV mapping routine. In one form, the fieldof view is based on a nominal position coordinate of the image sensorand a nominal orientation of the image sensor. In one form, theinstructions further comprise performing an augmentation routine on theplurality of synthetic images in response to labeling the plurality ofsynthetic images. In one form, the one or more adjusted field of viewsare based on an adjusted position coordinate of the image sensor, anadjusted orientation of the image sensor, or a combination thereof.

The present disclosure provides a method including generating one ormore nominal images of at least a portion of a digital twin of theenvironment, where the digital twin is a virtual representation of theenvironment, where the one or more nominal images are based on a fieldof view of an image sensor of the digital twin and one or more nominalcharacteristics of one or more components of the digital twin, and wherethe field of view is based on a nominal position coordinate of the imagesensor and a nominal orientation of the image sensor. The methodincludes defining one or more adjusted field of views of the imagesensor and generating one or more auxiliary nominal images of theportion of the digital twin of the environment based on the one or moreadjusted field of views and the one or more nominal characteristics,where the one or more adjusted field of views are based on an adjustedposition coordinate of the image sensor, an adjusted orientation of theimage sensor, or a combination thereof. The method includes defining oneor more anomalous characteristics of the one or more components,generating one or more anomalous images of the portion of the digitaltwin of the environment based on the field of view and one or moreanomalous characteristics of the one or more components, and generatingone or more auxiliary anomalous images of the portion of the digitaltwin of the environment based on the one or more adjusted field of viewsand the one or more anomalous characteristics. The method includesperforming a tessellation routine and a texture mapping routine on theone or more nominal images, the one or more auxiliary nominal images,the one or more anomalous images, and the one or more auxiliaryanomalous images to generate a plurality of synthetic images andlabeling, for each synthetic image from among the plurality of syntheticimages, the synthetic image as one of an anomalous type, a nominal type,or a combination thereof.

Further areas of applicability will become apparent from the descriptionprovided herein. It should be understood that the description andspecific examples are intended for purposes of illustration only and arenot intended to limit the scope of the present disclosure.

DRAWINGS

In order that the disclosure may be well understood, there will now bedescribed various forms thereof, given by way of example, referencebeing made to the accompanying drawings, in which:

FIG. 1 illustrates a functional block diagram of a system for inspectinga vehicle in accordance with the teachings of the present;

FIG. 2 illustrates a digital twin of an environment and one or morecomponents of a vehicle in accordance with the teachings of the presentdisclosure;

FIG. 3A illustrates a synthetic image associated with a nominalcharacteristic in accordance with the teachings of the presentdisclosure;

FIG. 3B illustrates a synthetic image associated with an anomalouscharacteristic in accordance with the teachings of the presentdisclosure; and

FIG. 4 illustrates an example routine for generating synthetic images ofa training database in accordance with the teachings of the presentdisclosure.

The drawings described herein are for illustration purposes only and arenot intended to limit the scope of the present disclosure in any way.

DETAILED DESCRIPTION

The following description is merely exemplary in nature and is notintended to limit the present disclosure, application, or uses. Itshould be understood that throughout the drawings, correspondingreference numerals indicate like or corresponding parts and features.

The present disclosure provides a synthetic image generation moduleconfigured to provide synthetic images that are utilized to train animage analysis system that performs various AI and ML routines, such asconvolutional neural network routines, deep neural network routines,computer vision routines, among others. The synthetic image generationmodule generates nominal images based on a field of view of an imagesensor of a digital twin representing the environment and one or morenominal characteristics of various components of the digital twin.Additionally, the synthetic image generation module generates anomalousimages based on the field of view and one or more anomalouscharacteristics associated with the components. Moreover, the syntheticimage generation module generates auxiliary nominal images and auxiliaryanomalous images based on one or more adjusted field of views and/ornominal/anomalous characteristics associated with the components.

The synthetic image generation module subsequently performs variousimage processing routines, such as a tessellation routine, a decimationroutine, a texture mapping routine, and an augmentation routine togenerate a plurality of synthetic images. The synthetic image generationmodule labels the synthetic images as an anomalous type, a nominal type,or a combination thereof in accordance with known training routines.Accordingly, the synthetic image generation module enables an operatorto virtually and arbitrarily define various anomalies using a digitaltwin associated with a component when training an image analysis system,thereby foregoing the need to define and construct real anomalies withactual components of the environment. As such, the synthetic imagegeneration module inhibits the amount of time and resources utilized totrain the image analysis systems.

Referring to FIG. 1 , an environment 1 (e.g., a manufacturing facility)including a quality control system 5 for detecting defects of a vehicle50 is provided. The quality control system 5 generally includes adigital twin module 10, a synthetic image generation module 20, ananomaly detection system 30, and a training database 40. It should bereadily understood that any one of the components of the quality controlsystem 5 can be provided at the same location or distributed atdifferent locations (e.g., via one or more edge computing devices) andcommunicably coupled accordingly. As an example, the anomaly detectionsystem 30 may be provided on at an edge computing device (e.g., asmartphone, a laptop, a tablet, among other mobile computing devices)and communicably coupled to the training database 40 via a wirelesscommunication protocol (e.g., a message queuing telemetry transport(MQTT) protocol, a Bluetooth®-type protocol, a cellular protocol, awireless fidelity (Wi-Fi)-type protocol, a near-field communication(NFC) protocol, an ultra-wideband (UWB) protocol, among others). Asanother example, the digital twin module 10, the synthetic imagegeneration module 20, and the training database 40 may be provided on afirst computing device, and the anomaly detection system 30 may beprovided on a second computing device communicably coupled to the firstcomputing device via the wireless communication protocol or a wiredcommunication protocol.

In one form, the anomaly detection system 30 includes image sensors 32and an image analysis system 34. In one form, the image sensors 32 areconfigured to obtain images of the vehicle 50. In one form, the imagesensors 32 are provided by exterior/interior image sensors of thevehicle 50, image sensors that are removable from the vehicle 50 when amanufacturing operation/process is completed, image sensors that aredisposed on a robot (e.g., a fixed robot, an autonomous robot, amongothers), and/or image sensors of a mobile computing device (e.g., asmartphone, a tablet, among others). Example image sensors 32 include,but are not limited to, a two-dimensional (2D) camera, athree-dimensional (3D) camera, a red-green-blue (RGB) camera, a stereovision camera, an infrared sensor, a radar scanner, a laser scanner,and/or a light detection and ranging (LIDAR) sensor.

In one form, the image analysis system 34 is configured to perform an AIroutine, an ML routine, a vision routine, and/or any other type of imageprocessing routine based on one or more synthetic images stored in thetraining database 40 to determine whether the vehicle 50 includes ananomaly (e.g., a component of the vehicle 50 includes a dent, scratch,or an alignment/installation error). Example AI routines include, butare not limited to, convolutional neural network-based routines, deepneural network-based routines, computer vision-based routines, amongothers. Additional details describing the generation of the syntheticimages stored in the training database 40 are provided below.

Referring to FIGS. 1-2 , in one form, the digital twin module 10 isconfigured to generate images of at least a portion of a digital twin100 (i.e., a virtual representation) of the environment 1. In one form,the digital twin 100 is a 3D digital model that includes various virtualrepresentations of systems/components within the environment 1, such asvirtual image sensors 110-1, 110-2, ... 110-n (collectively referred tohereinafter as “virtual image sensors 110”) that correspond to imagesensors 32 of the anomaly detection system 30. Specifically, the virtualimage sensors 110 are digital representations of the image sensors 32and thus, may correspond to the dimensions, 3D position coordinate, andorientation of the image sensors 32 within the environment 1. In oneform, the 3D digital model includes virtual robots 120 having virtualimage sensors 110 disposed thereon and that correspond to thedimensions, 3D position coordinates, orientations, and FOVs (illustratedby cones 123 in FIG. 2 ) of one or more robots of the environment 1 (notshown). In one form, the 3D digital model includes virtualinfrastructure elements 125 having virtual image sensors 110 disposedthereon and that correspond to the dimensions, 3D position coordinates,markings, and orientations of one or more additional elements of theenvironment 1, such as poles, beams, conveyors, workstations, amongother elements of the environment. In one form, the 3D model includes avirtual vehicle 130 that corresponds to the vehicle 50 and one or morecomponents thereof at various stages of a manufacturing routine. In oneform, the 3D digital model is a computer-aided design (CAD) file, astandard tessellation language (STL) file, and/or any other file typeconfigured to provide a 3D digital model of the environment 1.

In one form, the digital twin module 10 includes a FOV module 12, acharacteristic module 14, and an image generation module 16. In oneform, the FOV module 12 is configured to select one of the virtual imagesensors 110 of the digital twin 100. In one form, the digital twinmodule 10 may include a human-machine interface (HMI), such as an inputdevice (e.g., a keyboard, mouse, among other input devices), a graphicaluser interface (e.g., a touchscreen display), and/or other types of HMIsconfigured to receive inputs from an operator corresponding to theselection of the virtual image sensors 110.

In one form, the FOV module 12 is configured to identify a nominal FOVof the selected virtual image sensor 110. In one form, the nominal FOVis defined based on a nominal position coordinate (e.g., a 3D positioncoordinate) and a nominal orientation of the selected virtual imagesensor 110. Specifically, the nominal position coordinate and nominalorientation may be associated with a 3D position coordinate/orientationof the corresponding image sensor 32 when it obtains image data of thevehicle 50 for subsequent analysis by the image analysis system 34. Inone form, the nominal FOV is based on a pixel density, focal length,height, width, and/or geometry of the virtual image sensors 110.

In one form, the FOV module 12 is configured to adjust the nominal FOVto define one or more adjusted FOVs of the selected virtual image sensor110. The adjusted FOVs are defined based on an adjusted positioncoordinate and/or an adjusted orientation of the selected virtual imagesensor 110. To define the adjusted FOVs, an operator may provide inputsto the HMI of the digital twin module 10 corresponding to the adjustedor the nominal FOV.

In one form, the characteristic module 14 is configured to definenominal characteristics and anomalous characteristics (collectivelyreferred to hereinafter as “one or more characteristics”) of thecomponents of the virtual vehicle 130. As used herein, a “nominalcharacteristic of the component” refers to a characteristic (e.g., adimension, position, orientation, surface quality, state, type, and/orany other qualitative/quantitative feature) of the component thatsatisfies a predefined value and/or criteria. As used herein, an“anomalous characteristic of the component” refers to a characteristicof the component that does not satisfy a predefined value and/orcriteria. As an example, a vehicle clip (as the component of the virtualvehicle 130) may have a nominal characteristic associated with apredefined size, position, and orientation and being properly secured toa wire harness. Furthermore, the vehicle clip may have an anomalouscharacteristic associated with deviations from the predefined size,position, or orientation and/or not being properly secured to a wireharness. To define the one or more characteristics, an operator mayprovide inputs to the HMI of the digital twin module 10 corresponding tomanipulating the components of the virtual vehicle 130 to satisfy theone or more characteristics (e.g., using a mouse/keyboard to define thecharacteristics of vehicle clip) and importing CAD files with thecomponents having one of the anomalous/nominal characteristics (e.g.,importing CAD files of a vehicle clip with a proper connection and animproper connection between a wire harness and a fuel pump assembly).

In one form, the image generation module 16 is configured to generateone or more nominal images of at least a portion of the digital twin 100based on the nominal FOV and the one or more nominal characteristics ofthe one or more components of the virtual vehicle 130. In one form, theimage generation module 16 is configured to generate one or moreauxiliary nominal images of at least a portion of the digital twin 100based on one or more adjusted FOVs (i.e., a FOV that is unequal to thenominal FOV) and the one or more nominal characteristics of the one ormore components of the virtual vehicle 130.

The image generation module 16 is configured to generate one or moreanomalous images of at least a portion of the digital twin 100 based onthe nominal FOV and the one or more adjusted characteristics of the oneor more components of the virtual vehicle 130. In one form, the imagegeneration module 16 is configured to generate one or more auxiliaryanomalous images of at least a portion of the digital twin 100 based onthe one or more adjusted FOVs and the one or more anomalouscharacteristics of the one or more components of the virtual vehicle130.

The one or more nominal images and the one or more auxiliary nominalimages are collectively referred to hereinafter as “the nominal images.”The one or more anomalous images and the one or more auxiliary anomalousimages are collectively referred to hereinafter as “the anomalousimages.” As used herein, “a set of nominal and anomalous images” refersto at least one of the one or more nominal images, the one or moreauxiliary nominal images, the one or more anomalous images, and the oneor more auxiliary anomalous images.

In one form, the synthetic image generation module 20 includes atessellation module 22, a decimation module 24, a texture mapping module26, a labeling module 27, and a data augmentation module 28. In oneform, the tessellation module 22 is configured to perform a tessellationroutine on the set of nominal and anomalous images. As used herein, the“tessellation routine” refers to converting the components of thevirtual vehicle 130 into a plurality of vertices, edges, faces,surfaces, and materials that collectively form a polyhedralrepresentation of the components. The faces may have various shapes,such as triangles, quadrilaterals, convex polygons, and/or concavepolygons. In one form, the tessellation module 22 may be implemented byknown vector graphic rendering systems, such as OpenGL and/or Direct3D.

In one form, the decimation module 24 is configured to perform adecimation routine on the set of nominal and anomalous images. As usedherein, the “decimation routine” refers to compressing the one or moretessellated images by selectively removing vertices from the one or moretessellated images. In one form, the vertices are removed based on thesurface associated with vertices (e.g., vertices associated withsurfaces having a low curvature may be removed provided that thestructural integrity of the component is maintained), a predeterminednumber of faces (e.g., 100,000 faces/polygons), or a combinationthereof. It should be understood that the decimation module 24 may beremoved from the synthetic image generation module 20 in some forms.

In one form, the texture mapping module 26 is configured to perform atexture mapping routine on the set of nominal and anomalous images togenerate a plurality of synthetic images. In one form, the texturemapping module 26 performs the texture mapping routine in response tothe tessellation module 22 performing the tessellation routine on theset of nominal and anomalous images. In one form, the texture mappingmodule 26 performs the texture mapping routine in response to thedecimation module 24 performing the decimation routine on the set ofnominal and anomalous images. As used herein, the “texture mappingroutine” refers to defining a surface texture of the component byprojecting a 2D bitmap image or a procedural texture associated with thecomponent onto the set of nominal and anomalous images. As an example,the texture mapping module 26 may perform a UV mapping routine (as thetexture mapping routine). It should be understood that the texturemapping module 26 may perform other types of texture mapping routinesand is not limited to the example described herein.

In one form and referring to FIGS. 1 and 3A-3B, the tessellation module22 and the texture mapping module 26 (and optionally, the decimationmodule 24) may collectively generate synthetic images 310, 320associated with a vehicle clip 312, a wire harness 314, and a fuel pumpassembly 316. As an example, the digital twin 10 module provides anominal image or auxiliary nominal image having a nominal/adjusted FOVthat depicts a vehicle clip properly connecting a wire harness and afuel pump assembly (as the nominal characteristic) to the syntheticimage generation module 20. Accordingly, the tessellation module 22, thedecimation module 24, and the texture mapping module 26 may collectivelyperform the tessellation, decimation, and texture mapping routines onthe nominal/auxiliary nominal image to generate the synthetic image 310.

As another example, the digital twin 10 module provides an anomalousimage or auxiliary anomalous image having the nominal/adjusted FOV anddepicting a vehicle clip improperly connecting a wire harness and a fuelpump assembly (as the anomalous characteristic) to the synthetic imagegeneration module 20. Accordingly, the tessellation module 22, thedecimation module 24, and the texture mapping module 26 may collectivelyperform the tessellation, decimation, and texture mapping routines onthe anomalous/auxiliary anomalous image to generate the synthetic image320. While the synthetic images 310, 320 described herein are associatedwith a vehicle clip 312, a wire harness 314, and a fuel pump assembly316 of a vehicle, it should be understood that the tessellation module22 and the texture mapping module 26 (and optionally, the decimationmodule 24) may collectively generate synthetic images for othercomponents and/or other nominal/anomalous characteristics and are notlimited to the examples described herein.

In one form, the labeling module 27 is configured to label eachsynthetic image as one of an anomalous type, a nominal type, or acombination thereof. In one form, an operator and/or a classifier modulemay provide inputs to an HMI of the labeling module 27 corresponding tothe desired label (e.g., defining the synthetic images as the anomalousand/or nominal type using bounding boxes, semantic segmentation, and/orcuboidal annotation). As an example, an operator may label the syntheticimage 310 as a nominal type and the synthetic image 320 as the anomaloustype.

In one form, the data augmentation module 28 is configured to perform anaugmentation routine on the synthetic images and in response to labelingthe synthetic images. As an example, the data augmentation module 28 mayapply a kernel to the synthetic images to append the images with noise,blurs, colors, sharpness adjustments, rotations, and/or sizeadjustments, thereby improving the accuracy of the image analysis system34. It should be understood that the data augmentation module 28 may beremoved from the synthetic image generation module 20 in some forms.

In one form, the labeling module 27 or the data augmentation module 28store the labeled synthetic images in the training database 40, therebyenabling the image analysis system 34 to perform the training routinesdescribed above based on the synthetic images generated by the syntheticimage generation module 20 and, optionally, real images obtained fromthe image sensors 32.

Referring to FIG. 4 , an example routine 400 for generating and storingsynthetic images in the training database 40 is shown. As describedherein, the image analysis system 34 performs known training routinesbased on the synthetic images of the training database 40. At 404, thedigital twin module 10 generates a nominal image and an anomalous image.At 408, the synthetic image generation module 20 performs thetessellation routine, and the synthetic image generation module 20performs the decimation routine at 412. At 414, the synthetic imagegeneration module 20 performs the texture mapping routine, and thesynthetic image generation module 20 labels the synthetic images as ananomalous type, a nominal type, or a combination thereof at 416. At 418,the synthetic image generation module 20 performs the augmentationroutine on the synthetic images, and the synthetic image generationmodule 20 stores the synthetic images in the training database 40 at420.

At 424, the synthetic image generation module 20 determines whetheradditional synthetic images corresponding to additional FOVs need to begenerated. If so, the routine 400 proceeds to 428, where the digitaltwin module 10 adjusts the FOV and proceeds to 440. If the syntheticimage generation module 20 determines no additional synthetic imagescorresponding to the additional FOVs need to be generated at 424, theroutine 400 proceeds to 432.

At 432, the synthetic image generation module 20 determines whetheradditional synthetic images corresponding to additional nominal and/oranomalous characteristics need to be generated. If so, the routine 400proceeds to 432, where the digital twin module 10 defines the nextnominal and/or anomalous characteristic and proceeds to 440. If thesynthetic image generation module 20 determines no additional nominaland/or anomalous characteristics need to be generated at 432, theroutine 400 ends. At 440, the digital twin module 10 generates anauxiliary nominal image or an auxiliary anomalous image based on one ofthe adjusted FOV and/or the defined characteristic and proceeds to 408.

Unless otherwise expressly indicated herein, all numerical valuesindicating mechanical/thermal properties, compositional percentages,dimensions and/or tolerances, or other characteristics are to beunderstood as modified by the word “about” or “approximately” indescribing the scope of the present disclosure. This modification isdesired for various reasons including industrial practice, material,manufacturing, and assembly tolerances, and testing capability.

As used herein, the phrase at least one of A, B, and C should beconstrued to mean a logical (A OR B OR C), using a non-exclusive logicalOR, and should not be construed to mean “at least one of A, at least oneof B, and at least one of C.”

In this application, the term “controller” and/or “module” may refer to,be part of, or include: an Application Specific Integrated Circuit(ASIC); a digital, analog, or mixed analog/digital discrete circuit; adigital, analog, or mixed analog/digital integrated circuit; acombinational logic circuit; a field programmable gate array (FPGA); aprocessor circuit (shared, dedicated, or group) that executes code; amemory circuit (shared, dedicated, or group) that stores code executedby the processor circuit; other suitable hardware components (e.g., opamp circuit integrator as part of the heat flux data module) thatprovide the described functionality; or a combination of some or all ofthe above, such as in a system-on-chip.

The term memory is a subset of the term computer-readable medium. Theterm computer-readable medium, as used herein, does not encompasstransitory electrical or electromagnetic signals propagating through amedium (such as on a carrier wave); the term computer-readable mediummay therefore be considered tangible and non-transitory. Non-limitingexamples of a non-transitory, tangible computer-readable medium arenonvolatile memory circuits (such as a flash memory circuit, an erasableprogrammable read-only memory circuit, or a mask read-only circuit),volatile memory circuits (such as a static random access memory circuitor a dynamic random access memory circuit), magnetic storage media (suchas an analog or digital magnetic tape or a hard disk drive), and opticalstorage media (such as a CD, a DVD, or a Blu-ray Disc).

The apparatuses and methods described in this application may bepartially or fully implemented by a special purpose computer created byconfiguring a general-purpose computer to execute one or more particularfunctions embodied in computer programs. The functional blocks,flowchart components, and other elements described above serve assoftware specifications, which can be translated into the computerprograms by the routine work of a skilled technician or programmer.

The description of the disclosure is merely exemplary in nature and,thus, variations that do not depart from the substance of the disclosureare intended to be within the scope of the disclosure. Such variationsare not to be regarded as a departure from the spirit and scope of thedisclosure.

What is claimed is:
 1. A method for training an image analysis systemcomprising: generating one or more nominal images of at least a portionof a digital twin of an environment, wherein the digital twin is avirtual representation of the environment, and wherein the one or morenominal images are based on a field of view of an image sensor of thedigital twin and one or more nominal characteristics of one or morecomponents of the digital twin; defining one or more anomalouscharacteristics of the one or more components; generating one or moreanomalous images of the portion of the digital twin of the environmentbased on the field of view and the one or more anomalouscharacteristics; performing a tessellation routine and a texture mappingroutine on the one or more nominal images and the one or more anomalousimages to generate a plurality of synthetic images; and labeling, foreach synthetic image from among the plurality of synthetic images, thesynthetic image as one of an anomalous type, a nominal type, or acombination thereof.
 2. The method of claim 1 further comprisingperforming a decimation routine on the one or more nominal images andthe one or more anomalous images to generate the plurality of syntheticimages.
 3. The method of claim 1, wherein the texture mapping routine isa UV mapping routine.
 4. The method of claim 1, wherein the field ofview is based on a nominal position coordinate of the image sensor and anominal orientation of the image sensor.
 5. The method of claim 1further comprising performing an augmentation routine on the pluralityof synthetic images in response to labeling the plurality of syntheticimages.
 6. The method of claim 1 further comprising: defining one ormore adjusted field of views of the image sensor; generating one or moreauxiliary nominal images of the portion of the digital twin of theenvironment based on the one or more adjusted field of views and the oneor more nominal characteristics; and generating one or more auxiliaryanomalous images of the portion of the digital twin of the environmentbased on the one or more adjusted field of views and the one or moreanomalous characteristics, wherein the plurality of synthetic images arefurther based on the one or more auxiliary nominal images and the one ormore auxiliary anomalous images.
 7. The method of claim 6, wherein theone or more adjusted field of views are based on an adjusted positioncoordinate of the image sensor, an adjusted orientation of the imagesensor, or a combination thereof.
 8. The method of claim 6 furthercomprising performing the tessellation routine and the texture mappingroutine on the one or more auxiliary nominal images and the one or moreauxiliary anomalous images to generate the plurality of syntheticimages.
 9. The method of claim 1, wherein the image sensor is disposedon one of a robot and a structure of the digital twin.
 10. A system fortraining an image analysis system, the system comprising: a processor;and a nontransitory computer-readable medium including instructions thatare executable by the processor, wherein the instructions include:generating one or more nominal images of at least a portion of a digitaltwin of an environment, wherein the digital twin is a virtualrepresentation of the environment, and wherein the one or more nominalimages are based on a field of view of an image sensor of the digitaltwin and one or more nominal characteristics of one or more componentsof the digital twin; defining one or more adjusted field of views of theimage sensor; generating one or more auxiliary nominal images of atleast the portion of the digital twin of the environment based on theone or more adjusted field of views and the one or more nominalcharacteristics; defining one or more anomalous characteristics of theone or more components; generating one or more anomalous images of theportion of the digital twin of the environment based on the field ofview and one or more anomalous characteristics of the one or morecomponents; generating one or more auxiliary anomalous images of theportion of the digital twin of the environment based on the one or moreadjusted field of views and the one or more anomalous characteristics;performing a tessellation routine and a texture mapping routine on theone or more nominal images, the one or more auxiliary nominal images,the one or more anomalous images, and the one or more auxiliaryanomalous images to generate a plurality of synthetic images; andlabeling, for each synthetic image from among the plurality of syntheticimages, the synthetic image as one of an anomalous type, a nominal type,or a combination thereof.
 11. The system of claim 10, wherein theinstructions further comprise performing a decimation routine on the oneor more nominal images and the one or more anomalous images to generatethe plurality of synthetic images.
 12. The system of claim 10, whereinthe texture mapping routine is a UV mapping routine.
 13. The system ofclaim 10, wherein the field of view is based on a nominal positioncoordinate of the image sensor and a nominal orientation of the imagesensor.
 14. The system of claim 10, wherein the instructions furthercomprise performing an augmentation routine on the plurality ofsynthetic images in response to labeling the plurality of syntheticimages.
 15. The system of claim 10, wherein the one or more adjustedfield of views are based on an adjusted position coordinate of the imagesensor, an adjusted orientation of the image sensor, or a combinationthereof.
 16. A method comprising: generating one or more nominal imagesof at least a portion of a digital twin of an environment, wherein thedigital twin is a virtual representation of the environment, wherein theone or more nominal images are based on a field of view of an imagesensor of the digital twin and one or more nominal characteristics ofone or more components of the digital twin, and wherein the field ofview is based on a nominal position coordinate of the image sensor and anominal orientation of the image sensor; defining one or more adjustedfield of views of the image sensor; generating one or more auxiliarynominal images of the portion of the digital twin of the environmentbased on the one or more adjusted field of views and the one or morenominal characteristics, wherein the one or more adjusted field of viewsare based on an adjusted position coordinate of the image sensor, anadjusted orientation of the image sensor, or a combination thereof;defining one or more anomalous characteristics of the one or morecomponents; generating one or more anomalous images of the portion ofthe digital twin of the environment based on the field of view and oneor more anomalous characteristics of the one or more components;generating one or more auxiliary anomalous images of the portion of thedigital twin of the environment based on the one or more adjusted fieldof views and the one or more anomalous characteristics; performing atessellation routine and a texture mapping routine on the one or morenominal images, the one or more auxiliary nominal images, the one ormore anomalous images, and the one or more auxiliary anomalous images togenerate a plurality of synthetic images; and labeling, for eachsynthetic image from among the plurality of synthetic images, thesynthetic image as one of an anomalous type, a nominal type, or acombination thereof.
 17. The method of claim 16 further comprisingperforming a decimation routine on the one or more nominal images andthe one or more anomalous images to generate the plurality of syntheticimages.
 18. The method of claim 16 further comprising performing anaugmentation routine on the plurality of synthetic images in response tolabeling the plurality of synthetic images.
 19. The method of claim 16,wherein the texture mapping routine is a UV mapping routine.
 20. Themethod of claim 16, wherein the image sensor is disposed on one of arobot and a structure of the digital twin.